Electrical and Computer Engineering


Reinforced Weed Classification utilising utilising Context Data and Deep Generative Models

Simon Leminen Madsen

This project is part of the Innovation Fund Denmark project RoboWeedMaPS. The overall goal of RoboWeedMaPS is to substantially reduce the amount of herbicides in modern crop farming, which will benefit society, the environment and the farmer. To achieve this, a more efficient and precise deployment of herbicides is needed. The project will incorporate automated vision systems to assess the optimum weed treatment and thereby eliminate the need for intermediate manual decision-making and data processing.

This project seeks to apply machine learning, specifically deep learning, to automatically classify weed species and their current development stage from images. To improve the certainty of the classification, it should incorporate context relevant data such as site-specific cropping history, past weed registrations, etc. The project will also explore the potential of generating photo realistic image samples of weeds using deep generative models. These artificial samples are expected to be used for creating a more robust weed classification model. Generative models can potentially also be used to improve the quality of unfocused and blurred images.

ABOUT THE PROJECT


Project title:
Reinforced Weed Classification utilising utilising Context Data and Deep Generative Models

PhD student: Simon Leminen Madsen

Contact: slm@eng.au.dk

Project period: Feb 2017 to Jan 2020

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisor: Senior Researcher Rasmus Nyholm Jørgensen 

Research section: Electrical and Computer Engineering


Compact integrated terahertz sources

Pengli An

Science and technologies based on terahertz (THz) frequency electromagnetic radiation (100 GHz-30 THz) have developed rapidly over the last 30 years, to the extent that these now touch many areas from fundamental science to ‘real world’ applications. THz spectroscopy and imaging are currently active and rapidly expanding fields of research with applications in biological and medical fields, for example cancer tumor imaging and defence and security fields like the airport security scanners to detect explosives and illegal drugs.

However, many applications require hand-held or mobile THz devices but these are not commercially available yet. The most important challenge for THz technology is the development of compact and efficient THz sources providing continuous wave output power.

The aim of this project is to develop compact THz emitters. THz radiation can be generated by mixing photonic signals, but these optical components are bulky. Using photonic integration, we can put all these components on a single chip. This provides an efficient and reliable solution for THz sources in terms of cost and size. Therefore, we will develop a photonic integrated circuit (PIC) for THz signal generation in the optical domain. Our PIC will then be coupled to another chip containing THz antennas, developed by our partners, to convert the optical signal to a narrow beam of THz radiation. Our goal is to realise THz frequency tuning and beam steering all in one chip.

ABOUT THE PROJECT


Project title:
Compact integrated terahertz sources

PhD student: Pengli An

Contact: pengli_an@eng.au.dk

Project period: Jan 2017 to Dec 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Enabling ultra-reliable low latency communications in 5G

Jianhui Liu

The fifth generation (5G) systems are expected to underpin the Tactile Internet connectivity. The typical use cases of Tactile Internet include industrial automation, remote surgery and autonomous vehicle and so on. To enable these use cases, ultra-reliable and low latency communication (URLLC) is essential to promote the experiences of latency, reliability and availability into an unprecedented new stage.

The URLLC requires reliability of 99.999 percent with latency in the range of 1-10 ms depending on the use cases. It presents enormous challenges at multiple OSI layers and requires disruptive design approaches. One of the prospective solutions is the combination between Fog computing and Radio Access Networks (Fog-RAN). Fog computing extends the computing and storage capacity from distant cloud to the edge of networks which therefore can shorten the end-to-end latency significantly and is beneficial for latency-critical applications. Furthermore, Fog-RAN will provide a flexible network architecture for service-optimised network design and deployment. The aim of this project is to design novel protocols and algorithms to enable URLLC from the aspect of radio access and networking layer.

ABOUT THE PROJECT


Project title:
Enabling ultra-reliable low latency communications in 5G

PhD student: Jianhui Liu

Contact: jianhui.liu@eng.au.dk

Project period: Jan 2017 to Dec 2019

Main supervisor: Assoc. Prof. Qi Zhang

Research section: Electrical and Computer Engineering


Microwave Photonic Oscillators Integrated on an Optical Chip

Peter Tønning

”Timing is everything” is a saying usually reserved for delivering punch-lines. In the field of communication technology, the sentence is literally true and ultra-precise timing is key in advancing the present network society. The timekeepers of technology, oscillators, provide the clock frequency for computing systems, enable carriers for information in wireless communication and provide timing in GPS and radar systems. Oscillators can be realised in a number of different ways, electronic based, crystal based or, more recently, photonic based. 

The optoelectronic oscillator, realised for the first time 20 years ago, offers performance in the GHz-regime way beyond what can be realised by other oscillator types. An optoelectronic oscillator utilises a mix of electronic and photonic components as the name suggests to combine the existing electron-powered technological platform with the superior bandwidth and low-loss capabilities of photons in waveguides/fibers. So far, these systems have been reserved mainly for research purposes due to considerable size and cost as well as lack of robustness. Recent advance in photonic integrated circuits means that the realisation of an optoelectronic oscillator on a chip is within reach.

The goal of this project is to create an optoelectronic oscillator on a chip with a frequency in the GHz regime and phase noise performance better than the crystal based alternative. An oscillator at this frequency would allow for advances in e.g. Doppler radar technology and future high-speed wireless communication.

This research is done in close collaboration with PhD student Lars Nielsen so for another take on this project, see Lars Nielsen's project description.

ABOUT THE PROJECT


Project title:
Microwave Photonic Oscillators Integrated on an Optical Chip

PhD student: Peter Tønning

Contact: toenning@eng.au.dk

Project period: Nov 2016 to Oct 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Silicon photonics for future spintronic-photonic memory

Hanna Becker

The use of information technology is exponentially growing and has become so frequent that information processing, transmitting and storing accounted for estimated 5 percent of world electricity production in 2012. As this growth is expected to continue, we need to find a way to decrease energy consumption.

The goal of this project is the development of an innovative optical on-chip network. This effort is part of a collaborative project which addresses the need for an energy-efficient data storage device. This network will switch and direct light pulses to spintronic memory elements as well as illuminate them to ‘write’ data. The energy consumption of this device will be reduced by two orders of magnitude compared to present-day memory technology.

Integrated photonics offers a promising platform for new energy-efficient on-chip technologies with the number of photonic elements on optical chips increasing at a rate comparable to Moore’s law. Together with the recent discovery of magnetization reversal by short optical pulses, an optically switchable spintronic memory element becomes feasible. This enables the unique integration of photonics and spintronic memory elements. The project is conducted in collaboration with IMEC/Ghent University (BE), Radboud University (NL), SpinTEC (FR) and QuantumWise (DK).

ABOUT THE PROJECT


Project title:
Silicon photonics for future spintronic-photonic memory

PhD student: Hanna Becker

Contact: hanna.becker@eng.au.dk

Project period: Oct 2016 to Sept 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Microwave Photonic Oscillators Integrated on an Optical Chip

Lars Nielsen

Oscillations exist naturally everywhere in our everyday life, from sound waves to the periodic rotations of the earth, creating the dynamics of this world. In order to bring life into our electronic systems, we use electronic oscillators which generate alternating voltages and currents at a desired frequency. They are a fundamental part of almost every piece of electronic circuitry, and they enable, for example, the dynamic behaviour of computers, carriers for the transmission of data and time in clocks.

Electronic oscillators relying on the well-defined resonance of crystals, mainly quartz, have existed for decades. However, the increasing demands for low noise performance and high bandwidth in applications such as high speed analog to digital converters, radars and positioning systems, go beyond the limit of these oscillators.

Another type of oscillator is the optoelectronic oscillator which has gained more and more interest due to its ultra-low noise performance. An optoelectronic oscillator is based on the modulation of an optical carrier wave by a microwave signal which is generated through an optical feedback loop. The current optoelectronic oscillators outperform the crystal oscillators in the giga-hertz regime. However, currently they are based mainly on large discrete components such as optical fibers and are thus applicable to laboratory use only.

The aim of this project is to reduce the size of the optoelectronic oscillator by integrating it onto a photonic chip whilst retaining the ultra-low noise performance.

ABOUT THE PROJECT


Project title:
Microwave Photonic Oscillators Integrated on an Optical Chip

PhD student: Lars Nielsen

Contact: ln@eng.au.dk

Project period: May 2016 to April 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Supporting Multidisciplinary Development of Cyber-Physical Systems

Casper Thule Hansen

Systems consisting of both software and hardware are becoming a vital part of society, where they constitute cars, trains, medical devices and so forth. Such systems can be called Cyber-Physical Systems as they often involve cyber elements controlling physical processes.

When developing Cyber-Physical Systems it can be useful to create models of components, a model being an abstract description of a component. These models are then used in a Co-Simulation which is a simulation of coupled technical systems. Simulating the constituents that make up a given system can help identify undesired behaviour. This study will involve the development of the Co-Simulation Orchestration Engine which is the software responsible for orchestrating a simulation using models of components.

The Co-Simulation Orchestration Engine is part of the INTO-CPS project which is short for Integrated Tool Chain for Model-based Design of Cyber-Physical Systems. The purpose of the INTO-CPS project is to create a family of interlinked tools that support development of Cyber-Physical Systems from requirements to realisation in hardware and software.

ABOUT THE PROJECT

Project title: Supporting Multidisciplinary Development of Cyber-Physical Systems

PhD student: Casper Thule Hansen

Contact: casper.thule@eng.au.dk

Project period: Feb. 2016 to Jan. 2019

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Intraocular pressure measurement for the treatment of primary open angle glaucoma using self-powered System-On-contact-Lens (SOL)

Katrine Lundager

Primary open-angle glaucoma (POAG) is one of the leading causes of visual impairment and blindness, which affected more than 57.5 million people globally in 2015 especially the middle-aged and older people.

Increased intraocular pressure (IOP) is known to be a risk factor for the development and progression of the optic nerve degeneration and visual field loss characterising the disease. Therefore, patients at risk are subjected to regular examinations with an ophthalmologist to see if the changes in the optic nerve and the visual field are progressing. However, it is known that the intraocular pressure shows diurnal variations, and especially IOP peaks during night are suspected to constitute a particular risk factor for progression of the disease. Therefore, it might improve the treatment considerably if the IOP is continuously measured.

The purpose of this project is therefore to design an eye contact lens for continuous monitoring of the intraocular pressure, which envisions integratability with a drug-delivery contact lens in the future. The proposed system includes sensors, electronics (processing and communication) that is powered by solar cells, a Radio-Frequency Energy harvester and a piezoelectric harvester for sleeping state. By the use of this technique, primary open angle glaucoma can be monitored more closely and treated more accurately.

ABOUT THE PROJECT


Project title: 
Intraocular pressure measurement for the treatment of primary open angle glaucoma using self-powered System-On-contact-Lens (SOL)

PhD student: Katrine Lundager

Contact: klundager@eng.au.dk

Project period: Feb. 2016 to July 2019

Main supervisor: Assoc. Prof. Farshad Moradi

Co-supervisor: Michael Heimlich

Research section: Electrical and Computer Engineering


Compressed Sensing for Machine-type communication in 5G

Mehmood Alam

The future communication system has to cope with extremely diverse and heterogeneous use cases which lead to a number of challenging requirements like massive connections, data deluge, traffic management and inter cell interference. It becomes increasingly apparent that 4G will not be able to meet these requirements. 5G is required to support Mission Critical IoT Communication, Massive Machine-type Communication and Gigabit mobile connectivity.

Ultra-low latency and high reliability are the key challenges for Mission Critical Communication while for Massive Machine-type Communication the key challenges are massive connections, data deluge and energy efficiency. The conventional techniques are far behind to meet these requirements.  To address these challenges, a new paradigm of communication systems is required. A number of techniques have been regarded as the potential enablers to address these issues. One of the novel techniques is Compressed Sensing. Compressed Sensing exploits the sparsity of the signal to design an efficient system, which has been used in many different fields like astronomy, medical image processing, etc. A 5G system has some basic sources of sparsity like sparse traffic, multipath channels and compressible short messages which can be exploited to cope with the challenges in Mission Critical IoT communication and Massive Machine-type Communication.

The aim of the project is to leverage compressed sensing technique to achieve the challenging performance requirements of Mission Critical IoT Communication and Massive Machine-type Communication in 5G.

ABOUT THE PROJECT


Project title:
Compressed Sensing for Machine-type Communication in 5G

PhD student: Mehmood Alam

Contact: mehmood.alam@eng.au.dk

Project period: Jan. 2016 to Dec. 2018

Main supervisor: Assoc. Prof. Qi Zhang

Research section: Electrical and Computer Engineering


Controlling Sound Zones – with perceptually optimised multi-channel signal processing

Xiaohui Ma

Sound zone control will lead to a revolution in how we use audio systems: Several groups of people can enjoy different audio contents in a shared space at the same time without interrupting one another. Today this can only be achieved by wearing headphones, which would affect the conversation between people negatively.

Some work has been done in terms of creating separated sound zones [see IEEE SPM 81-91, March 2015, and references herein]. The key to sound zone control is the filtering applied to each loudspeaker signal. Three algorithms are widely adopted for filter design: acoustic contrast control, pressure matching and planarity control. However, there are still many unsolved problems that limit the further development of sound zones. This project will address three unsolved issues:

  • quantify and minimise the influence of nonlinear distortion in loudspeaker drivers.
  • quantify the influence of room reflections on the current sound zone control methods.
  • devise new perception based cost functions for sound zone control and devise accompanying perception optimised regularisation methods.

ABOUT THE PROJECT


Project title: 
Controlling Sound Zones – with perceptually optimised multi-channel signal processing

PhD student: Xiaohui Ma

Contact: xma@eng.au.dk

Project period: Nov. 2015 to Oct. 2018

Main supervisors: Prof. (Docent) Preben Kidmose, Jan A. Pedersen (Dynaudio)

Co-supervisors: Assistant Prof. Jakob Juul Larsen, Patrick J. Hegarty (Dynaudio)

Research section: Electrical and Computer Engineering


Open Platform for Big Data Analytics and Information Management

Jacob Høxbroe Jeppesen

Innovation in agro-technology is expected to be a major facilitator for implementing a sustainable and intensive crop production. The Future Cropping partnership is a collaboration between numerous companies and universities where a main goal is to expand the use of ICT in the agricultural sector.

This project will be investigating the design of an open platform for data mining and analytics, which will integrate data from distributed information sources to provide new technologies for modern high yielding and low emission precision farming. Emphasis will be on designing a robust platform with horizontal scalability for data mining, and to apply machine learning techniques for automatic classification of crop areas exhibiting non-optimal growth. Furthermore, this allows for holistic optimisation of the yield based on previously unknown extracted patterns.

ABOUT THE PROJECT


Project title:
Open Platform for Big Data Analytics and Information Management

PhD student: Jacob Høxbroe Jeppesen

Contact: jhj@eng.au.dk

Project period: Nov. 2015 to Oct. 2018

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Co-supervisor: Prof. Thomas S. Toftegaard

Research section: Electrical and Computer Engineering


Tool Automation for Model-Based Design of CPSs

This project, as part of the INtegrated Tool chain for model-based design of Cyber Physical Systems (INTO-CPS) project, explores how to automate the process of moving from a discrete abstract model to a realisation in a programming language. Automating the process between a discrete model and its realisation can reduce the risk of human error, when a validated model is manually realized in a programming language and additionally reduce the Time-To-Market for product development.

The focus of this thesis is code generation against distributed hardware architectures, enabling hardware in-the-loop (HiL), software in-the-loop (SiL) and Design Space Exploration (DSE) of Cyber Physical Systems (CPSs).

A model of a CPS is called a co-model, and consists of both discrete and continues models that are connected. This code generator will be part of tools which together enable a detailed and intelligent DSE of all models in co-simulation, which is possible by sweeping over relevant design parameters. Different models from different simulation engines will be connected using the Functional Mock-up Interface (FMI), and extending FMI with information about the design parameters will enable FMI-based co-simulation to cover large parts of the CPSs design life cycle.

ABOUT THE PROJECT


Project title:
Tool Automation for Model-Based Design of CPSs

PhD student: Miran Hasanagic

Contact: miran.hasanagic@eng.au.dk

Project period: Feb 2015 to Jan 2018

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Detection and Recognition of Wildlife and Humans in Pasture Fields using Non-stationary Imaging Technologies

Peter Christiansen

The goal of the project is to aid the development of self-driving or autonomous machinery for the agricultural domain. Autonomous machinery is largely possible today but requires, by law, a human operator to ensure human safety. This project will enable autonomous machinery by achieving human safety through automatic detection of humans using a normal and a heat sensitive camera.

Apart from human safety, the farmer needs a cost efficient system by avoiding non-living obstacles that may expose farming machinery, human properties and nature to damages. It is also desirable to ensure wildlife safety in modern farming machinery by automatic detection using a normal and a heat sensitive camera. Wildlife is - in today’s mowing operations - becoming more exposed due to increased working widths and speeds of agricultural machinery leading to the killing of hidden/camouflaged wildlife and contamination of harvested crops due to undetected dead wildlife. Using a camera for automatic detection of wildlife will help farmers avoid the killing of larger animals and contamination of harvested crops.

 

 

ABOUT THE PROJECT


Project title:
Detection and Recognition of Wildlife and Humans in Pasture Fields using Non-stationary Imaging Technologies

PhD student: Peter Christiansen

Contact: pech@eng.au.dk

Project period: Feb 2015 to Sep 2017

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Co-supervisor(s): Prof. (Docent) Henrik Karstoft

Research section: Electrical and Computer Engineering


Utilisation of Radar and Lidar Sensors for Detecting and Classifying Humans and Animals in Autonomous Agriculture

Mikkel Fly Kragh

Autonomous farming is the concept of automatic agricultural machines operating safely and efficiently without human intervention. Today, technology is available to automatically navigate and operate agricultural machinery, such as tractors and harvesters, more efficiently and more precisely than by manual human operation. However, a crucial deficiency in this technology concerns the safety aspects. In order for an autonomous vehicle to be certified for unsupervised operation, it must perform automatic real-time risk detection and avoidance in the field with high reliability.

This project seeks to apply active sensors in the form of radar and lidar (laser range scanner) to automatically detect humans and animals from a moving vehicle in a farming environment. Radar and lidar both provide precise and robust distance measurements, making three-dimensional positioning of objects possible. In addition, classification of detected objects into categories such as humans, animals and ground/vegetation is investigated.

 

 

ABOUT THE PROJECT


Project title:
Utilisation of Radar and Lidar Sensors for Detecting and Classifying Humans and Animals in Autonomous Agriculture

PhD student: Mikkel Fly Kragh

Contact: mkha@eng.au.dk

Project period: Jan 2015 to Dec 2017

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Co-supervisor(s): Assistant Prof. Henrik Pedersen

Research section: Electrical and Computer Engineering


Silicon Photonics for High-Bandwidth Wireless Links and Remote Sensing

The exponential growth of the use of mobile devices, the interconnected environment and ‘smart’ cities require a dramatic increase in network capacity over the next decade. By 2020, it is expected that 50 billion devices are connected to the internet, a large part of these wirelessly. The required wireless bandwidth is, however, far beyond what technologies like WiFi can deliver.

Governments all over the world have opened up new frequency bands for such wireless communications. The current level of technology is not able yet, though, to provide low cost and compact transceivers for these frequency bands, which is a crucial requirement for ubiquitous and mobile interconnected devices. Moreover, no feasible technology exists for the next generation higher frequency bands.

Optical chips are far more suitable than electronics to generate high-bandwidth signals at these frequencies, but until recently this technology was too experimental and not robust. Over the last few years, however, foundries with mature fabrication processes have been set up to fabricate silicon based optical chips. For the first time, we can now use this optical chip technology to realise low cost, compact and high-bandwidth transceivers that enable the next generations of wireless internet. This project aims to achieve scientific and technical breakthroughs required to leverage on silicon photonics co-design of transceivers for high speed wireless communications.

ABOUT THE PROJECT


Project title:
Silicon Photonics for High-Bandwidth Wireless Links and Remote Sensing

PhD student: Hakimeh Mohammadhosseini

Contact: hmohammadhosseini@eng.au.dk

Project period: Dec 2014 to Nov 2017

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Ultra-Low Power Device in Nano-Scale Technology for Biomedical Applications- Seizure Detection

Mohammad Tohidi

Therapeutic and prosthetic devices have emerged as a promising candidate for treatment of patients with neurological disorders ranging from epilepsy and Parkinson’s disease to motor impairments. The ability to acquire targeted neurological information from the brain is an essential requirement for the advancement of these systems. Thus, brain monitoring introduces key challenges for electronic systems in terms of both instrumentation and information extraction.

The focus of this project is to design a low-power and low-noise mixed-signal IC design in Nano-scale technologies such as CMOS, Fin-FET and Tunnel-FET (TFET). This design will be used to handle brain signal (EEG) acquisition and feature extraction from an analogue channel into the digital domain. The main focus of the project is on designing ultra-low power digital and analogue components especially for neurological disorders such as seizure in a system-on-chip (SOC).

In this system, an Instrumentation Amplifier is used to acquire the microvolt signals from electrodes in the presence of numerous physiological and environmental interferences. These amplified signals are processed using a DSP or custom digital/analogue circuits in CMOS technology in an extremely low power mode. In this project designing high-speed photonic Analog-to-Digital Converters (ADC) will be explored in collaboration with the photonics group.

ABOUT THE PROJECT


Project title:
 Ultra-Low Power Device in Nano-Scale Technology for Biomedical Applications - Seizure Detection

PhD student: Mohammad Tohidi

Contact: m.tohidi@eng.au.dk

Project period: Oct 2014 to Sept 2017

Main supervisor: Prof. (Docent) Jens Kargaard Madsen

Co-supervisor: Assoc. Prof. Martijn Heck and Assistant Prof. Farshad Moradi

Research section: Electrical and Computer Engineering


Scalable Energy Management Infrastructure for Aggregation of Households

Armin Ghasem Azar

The Smart Grid represents an unprecedented opportunity to move the energy industry into a new era of reliability, availability and efficiency that will contribute to our economic and environmental health. The Smart Grid will consist of controls, computers, new technologies and equipment working together and with the electrical grid to respond to our constantly changing electric demands. Also, demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based prices. This project aims at developing a novel ICT infrastructure for the implementation of Demand Response in households. This infrastructure will enable the shifting of energy consumption from high energy-consuming loads to off-peak periods with high generation of electricity from Renewable Energy Sources.

The chief purpose of this project is to develop a novel, comprehensive and optimal scheduling strategy for varied-specific households. In this strategy, the aggregator system will optimise and manage a large number of partial loads simultaneously according to the generation of electricity from Renewable Energy Sources to shift the households’ demands to off-peak hours. The scheduling strategy needs to take into account constraints from household comfort, grid stability, market mechanisms, etc. Also, trying to optimise conflictive objectives of households and aggregator simultaneously creates a multi-objective optimisation problem. As a result, the main question is how much and when the power consumption should be shifted taking into account the inclusion of scalable and diverse-characteristic householders, different appliance types and dynamic energy price strategies. Answering this question helps the householders to benefit financially and the aggregator to balance the system optimally.

ABOUT THE PROJECT


Project title:
Scalable Energy Management Infrastructure for Aggregation of Households

PhD student: Armin Ghasem Azar

Contact: aga@eng.au.dk

Project period: Sept 2014 to Aug 2017

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


Optimised Signal Processing of SkyTEM Data

Søren Rasmussen

The project is part of a collaboration between the company SkyTEM, the Department of Geoscience and the Department of Engineering at Aarhus University. SkyTEM is a technology leader in groundwater measurements using a helicopter-based TEM (Transient Electromagnetic Method) system for sub-surface exploration.

TEM consists of measuring the earth response to a magnetic signal generated by a large induction coil which, in this case, is towed by a helicopter.

For this project, SkyTEM is currently developing a new hardware platform that enables advanced processing of the measured signals. The purpose of this project is to explore and make use of the possibilities enabled by this new platform.

ABOUT THE PROJECT


Project title:
Optimised Signal Processing of SkyTEM Data

PhD student: Søren Rasmussen

Contact: sras@eng.au.dk

Project period: Aug 2014 to June 2017

Main supervisor: Assistant Prof. Jakob Juul Larsen

Research section: Electrical and Computer Engineering


Low Voltage/Low Power Design in Future Nodes (FinFET and Nanowire-based Devices)

Behzad Zeinali

The past few decades have seen the evolution of a semiconductor industry driven by technology scaling. Miniaturisation of bulk Field Effect Transistors (FETs) along with the scaling of power supply voltage have provided the benefits of higher performance, lower power and larger integration density. However, future scaling will face considerable challenges e.g. short-channel effects (SCEs) causing the design and optimisation of circuits to become very challenging.

One approach to counter these effects is to introduce alternate devices which possess inherently better robustness to SCEs in comparison to existing technology. Among these alternatives, multiple-gate FETs such as FinFETs or gate wrap-around FETs are emerging as promising candidates. FinFETs have the potential for analogue applications as well as for improving the performance of digital circuits such as static random access memories (SRAM) which are widely used in most digital and computer systems. In this respect, Intel will use the 3-D trigate transistors commercially in 22-nm technology node and so a strong interest has emerged among semiconductor industries in forming 14 and 10-nm bulk FinFET.

In this project, FinFET devices are utilised for SRAM modules in both circuit and device level designs. Also, we will investigate FinFET and its potentials for low-power applications and design some analogue and mixed-signal building blocks by FinFET in sub 14-nm technologies using the Design Kits provided by IMEC.

ABOUT THE PROJECT


Project title:
Low Voltage/Low Power Design in Future Nodes (FinFET and Nanowire-based Devices)

PhD student: Behzad Zeinali

Contact: beze@eng.au.dk

Project period: May 2014 to April 2017

Main supervisor: Assistant Prof. Farshad Moradi

Co-supervisors: Prof. (Docent) Jens Kargaard Madsen and Praveen Raghavan, IMEC

Research section: Electrical and Computer Engineering


Development of Methods for Objective EEG Analysis of Brain Activity induced by Sugar, Salt, Fat and their Substitutes

Camilla Arndal Rotvel

During the last decade, overweight and obesity have become an increasing global issue. According to WHO, in 2008, around 1.4 billion people over the age of 20 were overweight, at least 500 million were obese and at least 40 million children under the age of five were overweight.

The Food Industry's response to the obesity epidemic has been to produce a number of low fat and sugar food products that enable the consumer to eat the same food while consuming fewer calories. However, an investigation conducted by the Food Administration shows that people tend to consume extra-large servings of the light products, negating any benefits the light products might offer. 

A solution to the above-mentioned obesity epidemic requires a more thorough understanding of the brain's response to varying salt, sugar and fat levels and subjective satiation. Traditionally, food ingredient selection is based on physical and sensory analysis methods. However, in connection with salt, sugar and fat substitution products, objective measurement methods lack the ability to describe what we can register with our senses. In this regard, brain recordings are particularly interesting.

The idea behind the project is to utilise EEG methods to screen salt, sugar and fat substituents when selecting new food ingredients. The goal is to compare EEG results with physical or sensory data for new food ingredients with the hope of supplementing selection criteria for new food ingredients with objective physiological EEG responses.

ABOUT THE PROJECT


Project title:
Development of Methods for Objective EEG Analysis of Brain Activity induced by Sugar, Salt, Fat and their Substitutes

PhD student: Camilla Arndal Rotvel

Contact: caro@eng.au.dk

Project period: Sept 2013 to Aug 2018

Main supervisor: Prof. (Docent) Preben Kidmose

Co-supervisors: Stine Møller, DuPont Nutrition BioSciences, Ole Næsby Larsen, University of Southern Denmark and Troels W. Kjær, Roskilde Sygehus

Research section: Electrical and Computer Engineering